{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#定义爬取豆瓣网信息函数\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def parse_html(book):\n",
    "    headers = {\n",
    "        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'\n",
    "    }\n",
    "    response = requests.get(f'https://www.douban.com/search?q=' + book ,headers=headers)\n",
    "    #response = requests.get(f'https://book.douban.com/top250?start={num}', headers=headers)\n",
    "    soup = BeautifulSoup(response.text, 'lxml')\n",
    "    #print(soup)\n",
    "\n",
    "    #类型和名称\n",
    "    all_Type = soup.find_all('h3')\n",
    "    book_names = [Type.get_text() for Type in all_Type]\n",
    "    leixing = []\n",
    "    mingcheng = []\n",
    "    for piece in book_names:\n",
    "        if \"小组\" in piece:\n",
    "            continue\n",
    "        elif \"日记\" in piece:\n",
    "            continue\n",
    "        else:  #首先去除爬取到的多余信息\n",
    "            piece  = piece.strip().replace(' ', '')\n",
    "            piece = piece.strip().replace('[', '')\n",
    "            piece = piece.strip().replace('可播放', '')\n",
    "            piece = piece.strip().replace('可试读', '')\n",
    "            piece = piece.strip().replace('有电子版', '')\n",
    "            piece = piece.strip().replace('\\n', '')\n",
    "            piece = piece.strip().replace('\\xa0', '')            \n",
    "            str1 = piece.split(']')\n",
    "            if book in str1[0]:\n",
    "                continue\n",
    "            else:\n",
    "                leixing.append(str1[0])\n",
    "                mingcheng.append(str1[1])\n",
    "            #for i in str1:\n",
    "               # print(i)\n",
    "    #print(len(leixing))\n",
    "    #print(mingcheng)\n",
    "    \n",
    "   \n",
    "    #出版年份ok \n",
    "    all_year = soup.find_all('span', class_='subject-cast')\n",
    "    nianfen = []\n",
    "    book_years = [year.get_text() for year in all_year]\n",
    "    for piece in book_years:\n",
    "        str1 = piece.split(' ')\n",
    "        try:\n",
    "            nianfen.append(int(str1[-1]))  #在获得数据中摘取出版年份\n",
    "        except:    #抛出异常，排除没有出版年份（即对应位置数据不能转换成int型数据）的情况\n",
    "            nianfen.append(0)\n",
    "    #print(len(nianfen))\n",
    " \n",
    "   \n",
    "    #评价人数ok\n",
    "    all_people = soup.find_all('span',class_='')\n",
    "    book_people = [people.get_text() for people in all_people]\n",
    "    renshu = []\n",
    "    i=0\n",
    "    for piece in book_people:  \n",
    "        if '[' not in piece:\n",
    "            i +=1\n",
    "        else:\n",
    "            break\n",
    "    book_people = book_people[i::]    #摘除开头多余的信息\n",
    "    \n",
    "    for piece in book_people:        \n",
    "        if '日记' in piece:\n",
    "            continue\n",
    "        elif '小组' in piece:\n",
    "            continue        \n",
    "            #print(piece)\n",
    "        elif '人评价' in piece:   #摘取含有评价人数的元素\n",
    "            piece = piece.strip('(').strip(')')\n",
    "            piece = piece.strip(\"人评价\")   #去除字符串中多余指定字符\n",
    "            renshu.append(int(piece))\n",
    "        elif \"(\" in piece:  #对于“暂未上映”或者“尚无评价”的数据，将评价人数赋为0\n",
    "            renshu.append(0)        \n",
    "        #else:\n",
    "            #renshu.append(0)\n",
    "    #print(type(book_people))\n",
    "    #print(len(renshu))\n",
    "    #print(renshu)\n",
    "    \n",
    "    #评分ok\n",
    "    all_mark = soup.find_all('span', class_='rating_nums')\n",
    "    book_rates = [mark.get_text() for mark in all_mark]\n",
    "    #print(len(book_rates))\n",
    "    pingfen = []\n",
    "    for i in range(len(mingcheng)):  #不能用len(book_rates)，某些无评分的数据未导入，book_rates长度比其他列表短，会造成信息错乱\n",
    "        if renshu[i] ==0:\n",
    "            pingfen.append(float(0))  #对于无评分的数据将评分补成0\n",
    "            book_rates.insert(i,'0')  #\n",
    "        else:\n",
    "            pingfen.append(float(book_rates[i]))\n",
    "    #print(len(pingfen))\n",
    "    #print(len(mingcheng))\n",
    "    #print(pingfen)    \n",
    "    \n",
    "    content = []\n",
    "    content = [leixing, mingcheng, nianfen, renshu, pingfen]\n",
    "    return content\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#为方便观察爬取到的结果，将数据写入csv文件，定义写入格式\n",
    "import csv\n",
    "import pandas as pd\n",
    "#csv 写入\n",
    "\n",
    "def write_(book,content):\n",
    "    #Type.append('type')\n",
    "    #Name.append('name')\n",
    "    content[0].insert(0,\"类型\")\n",
    "    content[1].insert(0,\"名称\")\n",
    "    content[2].insert(0,\"出版年份\")\n",
    "    content[3].insert(0,\"评价人数\")\n",
    "    content[4].insert(0,\"评分\")\n",
    "    file1= open(book + '.csv','a+', newline='')\n",
    "    #a+:打开一个文件用于读写。如果该文件已存在，文件指针将会放在文件的结尾。文件打开时会是追加模式。如果该文件不存在，创建新文件用于读写。\n",
    "    #设定写入模式\n",
    "    csv_write = csv.writer(file1,dialect='excel')\n",
    "    #写入具体内容\n",
    "    #dataframe1 = pd.DataFrame({'Type':Type,'Name':Name})\n",
    "   \n",
    "    for val in zip(content[0],content[1],content[2],content[3],content[4]):\n",
    "        csv_write.writerow(val)\n",
    "    #csv_write.writerow(Type)\n",
    "    #csv_write.writerow(Name)\n",
    "    file1.close()\n",
    "    print (\"write over\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin\n",
      "[['电视剧', '电视剧', '书籍', '书籍', '电影', '电影', '电影', '电影', '书籍', '书籍', '电视剧', '电视剧', '电视剧', '电影', '电影', '电影', '电影', '电视剧', '电视剧', '电影'], ['红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '红楼梦', '金玉良缘红楼梦', '红楼梦', '百家讲坛：刘心武揭秘《红楼梦》', '新红楼梦', '红楼梦'], [1987, 2010, 1996, 2016, 2018, 1989, 2019, 1962, 1999, 2017, 1977, 1996, 2002, 2015, 1944, 1977, 1962, 2005, 2005, 1927], [90224, 36783, 194182, 1515, 2428, 2693, 0, 2602, 10435, 376, 0, 126, 185, 166, 296, 5229, 234, 670, 116, 0], [9.6, 6.0, 9.6, 9.7, 4.3, 7.9, 0.0, 9.0, 9.6, 9.3, 0.0, 5.9, 8.5, 6.9, 6.3, 7.4, 5.9, 8.0, 4.6, 0.0]]\n",
      "[['电视剧', '电视剧', '书籍', '电影', '书籍', '电影', '电视剧', '电视剧', '游戏', '电视剧', '电视剧', '电视剧', '电影', '电视剧', '电影'], ['水浒传', '水浒传', '水浒传', '水浒传', '水浒传（全二册）', '水浒传之英雄本色', '水浒传', '小戏骨：水浒传', '幻想水浒传2SuikodenII', '极道水浒传', '六合水浒传', '台湾水浒传', '水浒传：智取生辰纲', '水浒', '水浒之智取生辰纲'], [1998, 2011, 2009, 1972, 1997, 1993, 1977, 2018, 0, 1992, 2006, 1994, 1957, 1982, 2017], [35195, 18419, 1582, 1366, 37949, 18954, 53, 3668, 184, 0, 0, 0, 0, 1642, 0], [8.6, 7.8, 9.1, 6.9, 8.6, 7.1, 5.0, 8.0, 9.3, 0.0, 0.0, 0.0, 0.0, 8.1, 0.0]]\n",
      "[['电视剧', '电视剧', '电视剧', '书籍', '书籍', '书籍', '电视剧', '电影', '电影', '电视剧', '电视剧', '电视剧', '电影', '音乐', '音乐', '音乐'], ['三国演义', '三国演义', '三国演义', '三国演义', '三国演义（全二册）', '三国演义', '三国演义3D动画版', '三国演义', '十三支演义偃月三国传外传幽州幻夜', '三国演义第二季', '三国演义（精编版）', '三国', '小戏骨：放开那三国', '三国演义', '三国演义', '三國演義'], [1994, 2017, 2009, 1986, 1998, 2001, 2011, 1978, 2014, 2019, 1997, 2010, 2017, 0, 2010, 0], [58130, 614, 4338, 9016, 83356, 2475, 330, 0, 69, 0, 1963, 36373, 4435, 371, 44, 0], [9.3, 8.5, 9.1, 9.4, 9.2, 9.3, 8.6, 0.0, 5.2, 0.0, 9.2, 7.8, 8.0, 9.5, 8.7, 0.0]]\n",
      "[['电视剧', '电影', '电视剧', '电视剧', '电视剧', '电视剧', '电视剧', '电影', '电影', '电视剧', '电影', '电影', '电视剧', '电影', '电视剧', '电影', '电影', '电影', '电影', '电视剧'], ['西游记', '西游记之大圣归来', '西游记后传', '西游记', '西游记', '西游记', '西游记', '西游记女儿国', '西游记', '西游记', '西游记', '西游记', '西游记', '西游记', '西游记', '西游记', '大话西游之大圣娶亲', '大话西游之月光宝盒', '西游记', '新西游记第六季'], [1986, 2015, 2000, 1996, 1999, 2012, 2010, 2018, 2007, 2010, 2022, 1966, 1978, 1960, 2006, 1993, 1995, 1995, 1956, 2018], [123381, 466076, 32426, 20332, 31219, 7530, 2661, 110708, 1497, 473, 0, 362, 218, 162, 873, 209, 805222, 651137, 0, 15702], [9.5, 8.3, 7.5, 7.7, 9.0, 6.5, 5.3, 4.4, 5.1, 8.5, 0.0, 6.3, 5.2, 6.9, 5.9, 5.6, 9.2, 9.0, 0.0, 9.7]]\n",
      "end\n"
     ]
    }
   ],
   "source": [
    "#从豆瓣网上爬取四大名著相关产品的数据\n",
    "if __name__ == '__main__':\n",
    "    print('begin')\n",
    "    data1 = parse_html(\"红楼梦\")\n",
    "    data2 = parse_html(\"水浒传\")\n",
    "    data3 = parse_html(\"三国演义\")\n",
    "    data4 = parse_html(\"西游记\")\n",
    "    print(data1)\n",
    "    print(data2)\n",
    "    print(data3)\n",
    "    print(data4)\n",
    "    print('end')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "write over\n",
      "write over\n",
      "write over\n",
      "write over\n"
     ]
    }
   ],
   "source": [
    "#将数据写入.csv文件\n",
    "write_(\"红楼梦\",data1)\n",
    "write_(\"水浒传\",data2)\n",
    "write_(\"三国演义\",data3)\n",
    "write_(\"西游记\",data4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，读取csv文件并将信息按列分离\n",
    "import os\n",
    "import pandas as pd\n",
    "import csv\n",
    "\n",
    "def open_data(book):\n",
    "    mxdPath=r\"C:\\Users\\73916\\\\\" + book + r\".csv\"\n",
    "    file=open(mxdPath)\n",
    "    content = csv.reader(file)   ##reader(f)读取文件中的一行，read()只能读取一个字符\n",
    "    data = []\n",
    "    Type = []\n",
    "    Name = []\n",
    "    Year = []\n",
    "    People = []\n",
    "    Mark = []\n",
    "    for piece in content:\n",
    "        data.append(piece)\n",
    "    #print(data)\n",
    "    for piece in data[1::]:#第一行为各列名称，所以从第二行开始截取，即data[1]\n",
    "        Type.append(piece[0])\n",
    "        Name.append(piece[1])\n",
    "        Year.append(piece[2])\n",
    "        People.append(piece[3])\n",
    "        Mark.append(piece[4])\n",
    "    #print(content)\n",
    "    file.close()\n",
    "    content = [Type, Name, Year, People, Mark]\n",
    "    return content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['电视剧', '电视剧', '书籍', '书籍', '电影', '电影', '电影', '电影', '书籍', '书籍', '电视剧', '电视剧', '电视剧', '电影', '电影', '电影', '电影', '电视剧', '电视剧', '电影']\n",
      "['电视剧', '电影', '电视剧', '电视剧', '电视剧', '电视剧', '电视剧', '电影', '电影', '电视剧', '电影', '电影', '电视剧', '电影', '电视剧', '电影', '电影', '电影', '电影', '电视剧']\n",
      "['电视剧', '电视剧', '书籍', '电影', '书籍', '电影', '电视剧', '电视剧', '游戏', '电视剧', '电视剧', '电视剧', '电影', '电视剧', '电影']\n",
      "['电视剧', '电视剧', '电视剧', '书籍', '书籍', '书籍', '电视剧', '电影', '电影', '电视剧', '电视剧', '电视剧', '电影', '音乐', '音乐', '音乐']\n"
     ]
    }
   ],
   "source": [
    "#打开四个表格\n",
    "content1 = open_data('红楼梦')\n",
    "print(content1[0])\n",
    "content2 = open_data('西游记')\n",
    "print(content2[0])\n",
    "content3 = open_data('水浒传')\n",
    "print(content3[0])\n",
    "content4 = open_data('三国演义')\n",
    "print(content4[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，对于爬到的产品类型进行分析\n",
    "def ana_type(book,Type):\n",
    "    type_dict = {}\n",
    "    \n",
    "    for data in Type:   #统计各个类型的产品数量，不同类型作为字典的key\n",
    "        if data not in type_dict.keys():   #如果该key不存在，则将value值初始化为1\n",
    "            type_dict[data] = 1   \n",
    "        else:     #如果该key存在，则将value值加1\n",
    "            type_dict[data] +=1   \n",
    "\n",
    "    #print (type_dict)\n",
    "    num = []\n",
    "    typename = []\n",
    "    explode = []\n",
    "    print(type_dict)\n",
    "    for key,value in type_dict.items():\n",
    "        typename.append(key)\n",
    "        num.append(value)\n",
    "    for name in typename:\n",
    "        explode.append(0)\n",
    "    explode = tuple(explode)\n",
    "    #for key in type_dict\n",
    "    drawpie(book,typename, num,explode)\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，绘制饼状图\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import mpl#字体\n",
    "#设置字体\n",
    "   \n",
    "def drawpie(book,name,num,explode):#画饼状图\n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "    plt.title(\"豆瓣网《\" + book + \"》相关产品种类分布情况\")\n",
    "    sizes=num\n",
    "    colors='lightgreen','gold','lightskyblue','lightcoral'\n",
    "    #explode=0,0,0\n",
    "    #print(type(explode))\n",
    "    plt.pie(sizes,explode=explode,labels=name,\n",
    "            colors=colors,autopct='%1.1f%%',shadow=True,startangle=50)\n",
    "    plt.axis('equal')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'电视剧': 7, '书籍': 4, '电影': 9}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'电视剧': 10, '电影': 10}\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'电视剧': 8, '书籍': 2, '电影': 4, '游戏': 1}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'电视剧': 7, '书籍': 3, '电影': 3, '音乐': 3}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析四大名著相关产品的类型分布，并绘制对应的饼状图,类型为表格中的第一列(content[0])\n",
    "num1 = ana_type(\"红楼梦\",content1[0])\n",
    "num2 = ana_type(\"西游记\",content2[0])\n",
    "num3 = ana_type(\"水浒传\",content3[0])\n",
    "num4 = ana_type(\"三国演义\",content4[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，对于爬到的产品出版年份进行分析\n",
    "def ana_year(book,Year):\n",
    "    num = [0,0,0,0,0]\n",
    "    for data in Year:\n",
    "        data = int(data)\n",
    "        if data < 1979:\n",
    "            num[0] += 1   \n",
    "        elif data < 1990:\n",
    "            num[1] += 1\n",
    "        elif data < 2000:\n",
    "            num[2] += 1\n",
    "        elif data < 2010:\n",
    "            num[3] +=1\n",
    "        else:\n",
    "            num[4] += 1\n",
    "    #print (type_dict)\n",
    "    print(num)\n",
    "    drawyear(book,num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，绘制出版年份柱状图\n",
    "import matplotlib.pyplot as plt \n",
    "from pylab import mpl#字体\n",
    "#解决中文显示问题\n",
    "from matplotlib import mlab\n",
    "from matplotlib import rcParams\n",
    "\n",
    "       \n",
    "def drawyear(book, count):#画折线图   \n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei']         \n",
    "    plt.title(\"《\" + book + \"》相关产品出版年份统计\")\n",
    "    name_list = ['1979年以前', '1979-1990', '1990-2000', '2000-2010', '2010年之后']\n",
    "    plt.xlabel(\"时间\")\n",
    "    plt.ylabel(\"新出版产品数量\")\n",
    "    \n",
    "    #y1= [2,4,7,2,4]\n",
    " \n",
    "    plt.plot(name_list, count)\n",
    "    #plt.plot(x, y2)\n",
    "\n",
    "    #plt.xticks((0,1,2,3,4),('1979年以前', '1979-1990', '1990-2000' , '2000-2010', '2010年之后'))\n",
    "\n",
    "    #plt.bar(x = (0,1,2,3),height = distance,width = 0.35,align=\"center\")\n",
    "\n",
    "    #rect = plt.bar(x = (0,1,2,3,4),height = count,width = 0.35,align=\"center\")\n",
    "    \n",
    "    #autolabel(rect)\n",
    "\n",
    "    plt.show()\n",
    "    #print(count)\n",
    "    return count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 2, 3, 3, 6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 1, 5, 3, 7]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 1, 5, 2, 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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aU/MsWRjjo7TlGURFCKP6tKm03fgRKRw9Wciri3fVTmDG+JElC2N8UFBUwgershjevQXN6sdW2rZn20aMPLcF/1q0g5yT1rswdZslC2N8MOe7Axw5UcCY/mcObHsybkQKx/OL+NciOzvc1G2WLIzxQVp6Bi0axjI4uZlX7bu3asjVPVvx6uKdHDlR4OfojPEfSxbGeGl/Tj4LtmRzc7+2REV6/68zbngypwqLeWnBdj9GZ4x/WbIwxkulRQNv6efdIahSXZs3YFTvNry+ZBcHj+f7Jzhj/MyShTFeKClR0tIzGNCpCR2bJfg8/9jhyRQWK8/Ps96FqZssWRjjhWW7jrC7TNFAX3VomsAt/dry9rd72HvsVA1HZ4z/WbIwxgtpy0uLBraq9jIeGdYVRZkyb1sNRmZM7bBkYUwVcvML+Xz9Pq7t1Zp6MZHVXk7bxvH85IL2pC3PIOPIyRqM0Bj/s2RhTBU+WbOX/MISr6+tqMzDl3YlMkKYNGdrDURmTO3xa7IQkRYisqqC96JEZI+IzHcfPfwZizHVlZaeSUqL+vRqm3jWy2rRMI6fDezABysz2ZGdVwPRGVM7/N2z+AdQr4L3egLvqOpQ97HOz7EY47PN+4+zJuMYo1MrLhroqweHdiE2KpLnZlvvwtQdfksWIjIMOAHsr6DJQOAaEVkmIq+ISFQF7YwJmOnLM4iOFG6oomigL5rVj+XOizvyydq9bN5/vMaWa4w/+SVZiEgM8FvgyUqaLQeGq+oFQDRwVQXLuk9E0kUkPTs7u+aDNaYCBUUlfLgqk+HdW9C0iqKBvrp/SGfqx0Tx3OwtNbpcY/zFXz2LJ4HnVfVYJW3Wquo+93k6kOypkapOVdVUVU1NSkqq6TiNqdDs7w5w9GQho2tgYLu8RvEx3D2oE1+s38/6rJwaX74xNc1fyWI48LCIzAd6i8jLHtpME5FeIhIJjALW+CkWY6olLT2Dlg3jGJLsn52UXwzuRGK9aCbOst6FCX5+SRaqOqR04BpYDUwQkWfKNfsjMM19f4mqzvZHLMZUx76cUyx0iwZGRtTMwHZ5DeOiuW9IZ+ZsOsiqPUf98hnG1BS/X2fhJo2Nqvp0uenrVbWnqvZQ1af8HYcxvnh/hVs0MLWtXz/nzos60jQhhgnWuzBBzi7KM6Ycp2hgJgM7N6FDU9+LBvoiITaKB4d2YdHWQ3y747BfP8uYs2HJwphyvt15hD1Hql800Fc/G9iB5g1ieXbWFlS1Vj7TGF9ZsjCmnLT0DBrERnHl+dUvGuiLuOhIHr60K8t2HmHxNutdmOBkycKYMnLzC/l83T6u7X12RQN9desF7WidGMezszZb78IEJUsWxpTx8eq9nC4qYUwtHYIqFRsVyaOXJbNqzzHmbT5Yq59tjDcsWRhTxoz0DLq1aEDPGiga6Kub+7WlfZN4np1pYxcm+FiyMMa1aX8uazJzGN2/5ooG+iI6MoKxlyWzYW8uX22oqKSaMYFhycIYlz+KBvpqVJ82dE5KYOKsrZSUWO/CBA9LFsYAp4uK+WhVFiPObUGThJiAxREZIYwbnsLmA8f5dN2+qmcwppZYsjAGmL3xoFM0sJYHtj25ukcrzmnZgOdmbaGouCTQ4RgDWLIwBnCurWiVGMdgPxUN9EVEhPD48BR2HDrBR6v3BjocYwBLFsaw99gpFm71b9FAX11+XgvOb9OQSXO2UGi9CxMELFmYsPf+ikxU4ZZ+gT8EVUpEeGJENzKOnGJGemagwzHGkoUJbyUlStqKDC7s3JT2TeMDHc6PDO2WRN/2jZg8dyv5hcWBDseEOUsWJqwt3XmYjCOnGN3fv6XIq0NEeGJkN/bl5PPusj2BDseEOUsWJqylLc+gQVztFQ301UVdmjKgUxP+b/52ThVY78IEjiULE7ZyThXyxfr9XNerNXHRtVc00BelvYvs46eZtnRXoMMxYcyvyUJEWojIqkref0VElojI0xW1McZfPl7jFg3sHzwD255c0KkJg5Ob8eKCHeSdLgp0OCZM+btn8Q+gnqc3RORGIFJVLwQ6i0iyv4LIO13ESwu22yCh+ZG05Rmc07IBPdrUftFAXz0xshtHThTw+je7Ah2KCVN+SxYiMgw4AVRUEW0okOY+nwkMqmA594lIuoikZ2dnVyuWjXtz+Z8vNvHm0t3Vmt+Eno17c1mXlcPo1MAUDfRV73aNGN69OS8t2E7OqcJAh2PCkF+ShYjEAL8FnqykWQKQ5T4/ArTw1EhVp6pqqqqmJiVV7+ra0m788/O3c8K68Qbniu2YyIiAFg301bgRKeTmF/HK1zsDHYoJQ/7qWTwJPK+qxyppk8cPh6jq+zEWAMaPSOHIiQJes2582DtdVMxHq52igY0DWDTQV+e1TuSqHi3599c7OXqiINDhmDDjrw30cOBhEZkP9BaRlz20WcEPh556Abv8FAsAfdo35rJzmjN14Q5y860bH85mbTzAsZOFjA7ygW1PHh+ewomCIl5auCPQoZgw45dkoapDVHWoqg4FVgMTROSZcs0+Am4XkQnAaOAzf8RS1rgRKeScKuSVRdaND2dp6Zm0ToxjUNdmgQ7FZyktGnB9r9a8/s0uso+fDnQ4Joz4/ToLN2lsVNWny03PxRnkXgpcqqo5/o7l/DaJXHm+dePDWdaxUywKsqKBvho7PIWC4hJemL890KGYMBLQi/JU9aiqpqlqrd1DctyIFPIKipi6yLrx4ai0aODNQVQ00FedmiVwY582vPntbvbn5Ac6HBMmwu4K7pQWDbiuV2teW7yLQ3nWjQ8nJSVKWnoGF3UJvqKBvnrssmRUlSnztgY6FBMmwi5ZAIy9LJnTRcW8aN34sLJ0x2Eyj54Kirvhna12TeIZndqO6cszyDx6MtDhmDAQlsmic1J9buzblmlLd3Mg17rx4WJ6ulM08IrzWwY6lBrxyLCuiAiT52wLdCgmDIRlsgCnd1FcovzfPPtHCwc5J52igdf3Dt6igb5qlViPnw5oz3srM9l16ESgwzEhLmyTRbsm8Yzu3453lu2xbnwY+HhNFgVFJYxJbR/oUGrUg0O7EB0pTJpjYxfGv8I2WQA86nbjp8y13kWom56eQfdWDTm/TcNAh1KjmjeI446LOvLR6iy2Hjge6HBMCAvrZNEqsR63XdCeGSusGx/KNuzNYX1WLqNT29aJooG+un9IF+KjI3lutvUujP+EdbIAeOhSpxv/T+vGh6wZ6ZnEREYwqnfdKRroiyYJMdw9qBOfrdvHxr25gQ7HhKiwTxbNG8Rxx4VON37bQevGh5r8wmI+XJXFiPPqVtFAX90zuDMN46KYMGtLoEMxISrskwXA/Zd0oV50JBOtGx9yZm08QM6pQsaEwLUVlUmsF829gzsz+7sDrMmorNizMdXjU7IQkcs9TOtXc+EERpOEGO66uBOfrd3Hd/usGx9K0tIzaJ0Yx8V1sGigr+4a1InG8dE8a70L4wdVJgsRae3eS7sJ8JiIdBORc0WkrYhcD/zV/2H6372DO9MgLoqJ9o8WMjKPnuTrbYe4ObVdnS0a6Iv6sVE8cEkXFm7JJn3XkUCHY0KMNz2LdGAl8GsgF/hf4CvgPmA8cMpv0dWixHinGz9z4wHWZlo3PhS8vyILVbilX9tAh1Jrfn5hR5rVj+XZmbbTY2qWN8lis/vYBCiwENgGHAJK/Bda7bvr4o40jo+2QcIQUFKizFiRwcVdm9KuSd0uGuiLejGRPHxpF5bsOMw32w4FOhwTQnwZs1BAgGSgOdAP577ZHu+dDSAiTURkhIjUiQPGDeKiuf+SLszfnM2K3daNr8uWhFDRQF/95IL2tEqM49lZW1DVQIdjQsTZnA2lZR5nEJHGwKfABcA8EUny0CZKRPaIyHz30eMs4qkRP7+wA83qx1g3vo6bvjyDhnFRXH5eaBQN9EVcdCSPDOvKit1Hmb8lO9DhmBBRnWSxBTiAM45x0H140hMYr6p/xhnj6FtBm3dKb8GqquuqEU+Nio+J4qGhXflm+2G+2W7d+Loo52QhX27Yz6g+bUKmaKCvbunXjraN6zHRehemhniTLM4BugPn4RyGugRIAZoBFf4nquoCVV0qIkNwehdLPDQbCFwjIstE5BURifL1C/jDbQPa07JhHBNm2j9aXfQft2hgOB6CKhUTFcFjlyWzNjOHWRsPBDocEwK8SRapQA/gGaAR8CtgJPAv4N9AhaOH4hTiGQMcBQo9NFkODFfVC4Bo4CoPy7hPRNJFJD07u3a61KXd+PTdR1m41XoXdc305Rmc26oh57dJDHQoAXVjnzZ0apbAhFlbKCmxnR5zdqpMFqqapaqHVPUo8LaqblLVjaqaoaqvAf9Vybyqqg8Da4HrPDRZq6r73OfpOIPn5ZcxVVVTVTU1KemMYQ+/GZ3qdOMnzNxsvYs6ZH1WDhv2OkUDw11UZASPD09m0/7jfL5+X9UzGFMJr8YsxJGqqm94eNvjRQki8isR+bn7slEF7aaJSC8RiQRGAWu8iac2xERF8NiwZNZk5jD7u4qGZUywmZGe4RQN7BOaRQN9dU3P1qS0qM/EWVsott6FOQu+DHD/R0QmiMgjItIfvi/18XYF7acCt4vIQpyxjUwReaZcmz8C04DVwBJVne1b+P51Y982dGwab934OiK/sJiPVu9l5HktaBQfukUDfREZIYwbnsL27BP8Z3VWoMMxdZg35T5EneMw24CXcM6GulJEVgJTgFs8zaeqR1V1hKoOUdWHVHWDqj5drs16Ve2pqj1U9amz/zo1y+nGp/Ddvly+WL8/0OGYKswsLRrYP3wHtj25/LyWnNuqIZPmbKWwOKSuozW1yJuexRci8h6QCHTFOYMpFfgYyARqbyAhAK7t1Zrk5vWZONu68cFuRnoGbRrV4+IudeIa0FoTESE8MTKF3YdP8v6KzECHY+oob5LFzcDvcE59/SPOabA3qOrvcQa3n5VQvP2YKzJCGDcihW0H8/hkzd5Ah2Mq8H3RwH5tiQiDooG+GnZOc3q3a8Tkuds4XVQc6HBMHeRNsngIeBxngHot8BSwU0T+B5gM/EpD/HShK85rSfdWDXlu9haKrBsflN5z95hvDqOigb4QEcaPSCHr2CnSlmcEOhxTB3mTLBrilPS4CIgBonDOWloNdAQ2+Cu4YBERITwxIoVdh0/ywUobJAw2JSXKjPRMLu7SLKyKBvpqcHIzLujYhMlzt5FfaL0L4xtvksVsYCfQB6f67A1AL+AKnHLlv/FbdEHksu7N6dWuEZPmbKWgyHoXwWTx9kNkHTvFLXZtRaVEhPEjUzh4/DRvLt0d6HBMHeNNsrgUKMA586kbzqmu24ClqjoN6CIiIX971rLd+Onp1o0PJmnpmSTWiw7LooG+Gti5KYO6NuOF+ds5cboo0OGYOsSbK7h/B7yIkyzGA3nA06r6ktvkQVUNi13tIcnN6N+xMVPmbrVufJA4drKArzbsZ1Tv1mFbNNBX40emcPhEAa8v2RXoUEwd4lWPQFVPqOpeVT0A5AOb3XtVJOH0OsKC07voxoHc07z17Z5Ah2OA/6zeS0FRCbeEcdFAX/Vt35hLuyXx0oId5OZ7KtlmzJmqc/joQ+DvwLM4Z0ddVKMRBbkLuzTl4q5NeWH+Nk4WWDc+0KYvz+C81lY00FfjR3Qj51Qh//56Z6BDMXWEN1dwvyQik0XkBRHpCYiq3q2qdwHbgq1ER20YP6Ibh/IKeP0bGyQMpPVZOWzclxvWpcirq0fbRC4/rwWvLNrJsZNhc3DAnAVvehY9gX/gnBHVDkBE2olIByBWRMLuP7Vfh8YM7ZbESwu3c9y68QGTlp5BTFQE1/duHehQ6qRxI1LIKyhi6sIdgQ7F1AHeJItTqrobOIxzvcV/gP8GngCWAb/2X3jB64kR3Th2spBXF+8KdChhKb+wmI9WZXH5eS2taGA1ndOyIdf0bM1r3+zicN7pQIdjglx1xiwycYoJbgF2uI+w06NtIiPPbcG/Fu0g56T1LmrbVxv2k5tfxBg7BHVWHh+eTH5hMS8u2B7oUEyQ8yZZRItIEyAB57aqD+Fcwb0OuAPPt0sNC+NHppB3uoh/LQrLfBlQM9IzadOoHhd1aRroUOq0Lkn1uaFPW95YspsDufnlvArrAAAgAElEQVSBDscEMW+SxUFgIk4Bwf0AqrpQVRcAOaq62I/xBbVzWjbk6h6teHXxTuvG16KMI07RwFtSrWhgTRh7WTLFJcrz87YFOhQTxLy5KO8mVb1DVX+mqitwbnHxbxF5FUgRkX/7P8zg9fjwFE4VFvOSDRLWmvdWZCJiRQNrSvum8dyS2o53lmWQdexUoMMxQao6YxbX4wxw/xI4n0oGuN0L90aISMjeYKBr8/qM6tOGN5bs4qB14/2uuER5b0Umg7o2o21jKxpYUx4d1hWAKXO3BjgSE6y8vQd3rPtTgGRVPaSqh92fByqYpzHwKc7hq3nu1d6e2r0iIktE5GlP79cFYy9LprBYeX6+DRL62+JtTtFAu7aiZrVuVI+fXNCOGemZ7D58ItDhmCDkzUV5kcCXAO59K/7h5bJ7AuNV9c/AV0BfD8u+EYhU1QuBziKS7G3gwaRD0wRGp7bl7W/3sNe68X6Vlp5BYr1oRpzbItChhJyHL+1KZIQwaY71LoJNfmExeQEu/OjNmEUxoCLyqogMwLnWokqqukBVl4rIEJzehaezpoYCae7zmcAgr6IOQo8Mc/LcFBsk9JujJwqYueEAN/RpY0UD/aB5wzh+fmEHPlqVxbaDeYEOx7iW7zrCVZMW8cdPAnvrIG/HLASYDjziy8Ldw1ZjgKOAp4sREoDSuwkdAc7YXRSR+0QkXUTSs7Ozffn4WtWmUT1uvaAdacsz2HP4ZKDDCUn/WZ1FQXGJ3bfCjx64pAtx0ZE8N3tLoEMJe3mni/h//1nPLS8uoaC4hGt7BbZSQaXJQkRaiMg0oFBVv1TV231ZuDoexik4eJ2HJnlAPfd5fU/xqOpUVU1V1dSkJI/DHkGjtBv/TxskrHGqyvT0TM5v05DzWlvRQH9pWj+Wuy7uyKdr97Fpf26gwwlbC7Zkc/nEhUxbups7L+rIV48PYXByYLd/VfUscnFudoSINBOR671dsIj8SkR+7r5shHMP7/JW8MOhp17ALm+XH4xaNIzj9oEd+GBlJtuzrRtfkzbszeU7KxpYK+4d3JkGsVFMnGW9i9p27GQB49NWc8e/lxEXHcF7D1zI7687j4TYqECHVnmyUNVTqjoT577b/wF8uRXZVOB2EVkIRAKZIvJMuTYfuW0mAKOBz3xYflB6YKjTjZ8023oXNWn6crdoYK82gQ4l5DWKj+GewZ35asMB1mXmBDqcsKCqfL5uH8MnLODj1Xt5dFhXPh87mH4dmgQ6tO95O2ahqnqxe3e8FiJyg4ikikiF6U5Vj6rqCFUdoqoPqeoGVX26XJtcnEHupcClqlrn/zKb1Y/lzos68snavWzefzzQ4YSE/MJi/rM6iyvOa0lifHSgwwkLdw/qSKP4aCbM2hzoUELewdx8HnhzBQ+9tZJWifX4+JFBPDGyG7FRwXUShzenzgrOzY5KvYpzyOgeYKWI/OZsAnCTSpqq7j+b5QST+4Z0pn6MdeNryvdFA/vbIaja0iAumvuGdGbe5mxW7D4a6HBCkqqSlp7B8AkLmL85myevPIcPH7qIc1s3DHRoHnlz6qyq6pdlXv9VVX+vqg/gnBKrIhKc3y5AGsXH8IvBnfhyw37WZ9X5zlLApaVn0LZxPS7sbEUDa9OdF3WkWf0Y6134QcaRk9z+yjL++721nNOyIV+MHcwDl3QhKrI6RTVqhzc9iwpPPVHVfGAeYFeilXP3oE4k1otmgvUuzkrGkZMs3naYW/q1s6KBtSw+JooHLunC4m2HWbL9cKDDCQnFJcqri3cycuJCVmcc40+jzufd+wbSOal+oEOrkjdpbDKAiAwQkaUi8o6IPOWOWcTjVKS1nkU5Dd1u/NxNB1m5x7rx1TUjPcMpGmjXVgTEzwZ2oEXDWCbM2oxTwMFU17aDx7nlxW/4wycbGdC5CTPHDeH2gR3qzE6QN8migfszCvgcuAuYDTwF7Ab+rqq22+HBnRd1pGlCjI1dVFPZooFtGtWregZT4+KiI3nk0q4s33WURVsPBTqcOqmwuITJc7Zy1aSv2XnoBM+N6c2rd/andR37m/YmWZQ9UHwF8AZOpdl1wP3AwyISvAfaAighNooHh3Zh0dZDfLvD8qmvvt52iL05+TawHWCj+7ejTaN6PDvTehe+WpeZw7WTv+bZWVsYeV4LZo2/hFF92uCcN1S3eLORj3fvXfEbIAmndMdaYCuQjnOtREd/BVjX/WxgB5o3iOXZmVvsH81HaekZNIq3ooGBFhsVyWOXdWVNZg5zvjsY6HDqhPzCYv7ni++4/v++5siJAqbe3o8pt/WlWf3YQIdWbd4ki+HAwzg1niYA/w8nQQA8AzRTVbvzTwXioiN5ZFhXlu06wtfbrBvvraMnCpi14QCjercJuvPNw9GNfdvSoWk8E2ZtoaTEdnoq8+2Ow1w5aREvLdjB6NR2zBp/CSPP8+V65uDkTbKY4j4mA6Pd+1dMxbkJUgLQx3/hhYYx/dvROjHOehc++MgtGmjlPYJDdGQEjw9PZuO+XL7cEDKXRNWo4/mFPP3ROsZMXUpxifLWPQP46009SawXGheSepMsHgEecB+lW7rjqnqze8tVTwUCTRlONz6Z1RnHmLfZuvFVUVWmL8+gR5vEoL1AKRxd16sNXZvXZ+KsLRRb7+JH5m06yOUTF/L2t3u4Z1Anvnx8MBd3Da0bhHqTLF4G3gJeA46LyHSgUEQ+E5FPReQxG+Cu2k392tK+Sbz1LrywPiuXTfuPM9pOlw0qkRHC48OT2Xowj0/W7A10OEHhyIkCxk1fzV2vLSchNor3H7yIp685l/iYwBf+q2lVfiNVvbmi90SkPk5Np5IajSoElXbjx6et4asN+7ni/FaBDiloTU/fQ2xUBNf1tqKBweaq81txTsttTJqzlWt6tgrqK479SVX5dO0+fv/xBnJOFTL2smQeurRLSI+vndVvWlXzVPWTmgom1F3fuw1dkhKYYN34CjlFA/dyxfktQ+ZYbyiJiBDGj0hh56ETfLAqq+oZQtCB3HzufWMFj76zijaN6/HpY4MYNyIlpBMFeNGzEJHmwFVACRADHADGuq/BuYf2ZX6LMIQ43fgUHn1nFZ+u3cv1tud8hi/X7+d4fhFjbGA7aI04twU92yYyafZWRvVuQ0xUePQuSsfS/vz5dxQUlfDUVd256+KOYdO78uZbKk5iKAbG4dxz4gqcBBIDjPRbdCHo6h6tOKdlAybN3kpRsR29Ky8tPYN2Teox0IoGBi0Rp3eRdewUaekZgQ6nVuw5fJKfvvwtT36wjvNaN+Srx4dw75DOYZMowLuqs9mq+oaqvgUcVNUSVS1S1SKgRFWL/R9m6IiIEMaNSGHHoRN8GKbd+IrsOXySb7Zb0cC64JKUJPp1aMyUudvILwzdTUBxifLyoh2MfG4B6zJz+MsNPXj7noF0bJYQ6NBqnTeHoe7C6VUIzo2PSm+VKvh25zzjGnluC3q0SeSfc7cyqk8bosNo76QyM1a4RQP72VlQwU5EeGJkCrf961ve/nYPdw/qFOiQatzm/cf51ftrWZ1xjMvOac4zN5xPq8S6Vc+pJnmzlYrESQx4+FkhEUkUkS9EZKaIfCgiMR7aRInIHhGZ7z56eB963SUijB+ZQsaRU8xIzwx0OEGhtGjg4OSkOldgLVxd1KUZF3ZuyvPzt3OyoCjQ4dSYgqISnpu9hWsmL2LPkZNMurU3L9+RGtaJArw7DPWyqr6uqq8D+9xDUqWvD1Qy60+BCao6EtiPM85RXk/gHVUd6j7WVedL1EVDU5Lo274Rk+duDeluvLcWbc1mX06+DWzXMU+MTOFQ3mneWLI70KHUiDUZx7h28tc8N3srV/VoxaxxQ7i+d90s/FfTvLn5UTMRuUZErubHFWjhhyu6z6Cqz6vqLPdlEuDp0uWBwDUiskxEXvF0T28RuU9E0kUkPTs7u6pw6wwR4Zcju7EvJ593l+0JdDgBNyM9k8bx0Qw/t3mgQzE+SO3YhEtSknhpwXaO5xcGOpxqO1VQzJ8/28gNzy8m51QhL/88lUm39qFpHS78V9O8OQxVD0gBugJvisgY95DRPKB9VTOLyIVAY1Vd6uHt5cBwVb0AiMY5w+pHVHWqqqaqampSUpIX4dYdF3VtxsDOTZgybzunCsK3d3HkRAEzN+5nVB8rGlgXjR+RwtGThby6eFegQ6mWJdsPc8Wkhfxr0U5uvaA9M8cPYbhVOj5DpclCRBoB3VV1AvACcFhVp7uHjC5V1a5VzN8EpwDh3RU0Wauq+9zn6UCyb+HXfU+M7MahvNNMW7or0KEEzEersigsVisaWEf1ateIEee24F+LdpBzsu70LnLzC/n1B+v4yb+c/dh37h3IX27oQcM4uxjUk6p6FtcAf3KfC/C0iLxd7vG+pxndAe0ZwK9VtaIDmtNEpJeIRAKjgDXV+A51Wv+OTRiSksSLC3aQdzp0Bgm9paqkpWfQs20i3VtZ0cC6avyIFI7nF/Hy13XjbgWzNx5g5ISFTF++h/uGdObLsUO4sItd21OZqpLFEpxDUKWW4lShfbTM47EK5v0F0Bd4yj1s9TsReaZcmz8C04DVwBJVne1j/CFh/IgUjpwo4LXFOwMdSq1bl5XDpv3HucV6FXVa91YNubpHK/799U6OnCgIdDgVOpx3msfeWcU9b6TTKD6aDx+6mN9c1Z16MXb4sypVXWexB2goImk4g9mpwP8Cx4D1wAJV9biFU9UXcA5dVUhV1+OcERXWerdrxPDuLZi6cAe3X9gxrGoiTV+e4RQN7NU60KGYszRuRDJfrN/HSwu28+urugc6nB9RVT5es5fff7yBvNNFjBuewoNDu4RNqZKaUOmaUtVCnFumvgw8BPwN+AOQgVMC5Dm3ZLk5S+NHpJCbX8Qri+pGN74mnCoo5uPVe7nSigaGhK7NG3B97za8vmQXB4/nBzqc7+3LOcU9r6cz9t3VdGiawGePDWbs8GRLFD7yZm3tB57CqQH1C1XNAG4GOgHLcJKIOUvntm7IVT1a8u/FuzgaxN34mvTlhn0cP13E6P52CCpUjL0smcJi5fl52wMdCiUlylvf7mbEhIV8s/0wv73mXN5/8CJSWjQIdGh1UlVnQw0AfuO+TATaich9QHOcK7uzgc5+jTCMjBuewomCIl5aGB69i+nLM2jfJJ6BnWxgMVR0bJbAzX3b8va3e9h77FTA4th16AS3vbyUpz5cT692iXz1+BB+MagTkVZzrNqq6lkUAxvdn98BO4HDQCFwHMgBJvkzwHCS3KIB1/dqzevf7CL7+OlAh+NXuw+fYOmOI9zSr60VDQwxj17WFUWZMm9brX92UXEJUxdu5/LnFrJhby5/u6kHb/5iAO2bxtd6LKGmqjGLdOADnAvmerg/PwCOAucD5+IckjI1ZOzwFAqKS3hhfuC78f40Iz3TKRpot04NOW0bx3Nr//akLc8g48jJWvvc7/blcuML3/CXzzcxJCWJ2eMvYUz/9laqo4Z4M2ZRAjyqqlOAl9S5gfSTqnoHsAU45M8Aw02nZgnc1LcNb367m305gevG+1Np0cAhyUlhX5wtVD18aVciIoR/ztnq9886XVTMhFlbuHby12QdPcWU2/ow9fZ+tGgY5/fPDifeFBJUVV3tPn/F/bnY/fmWqobHaGwtenRYMqrK/wWgG18bFm7NZn9uPmNsYDtktUyM4/aBHXh/ZSY7svP89jkr9xzlmn9+zT/nbOW6Xq2ZPf4SrunZ2noTfmDnjgWhdk3iGdO/HdNruRtfW2akZ9A4PprLulvRwFD24NAuxEZFMskPvYuTBUX86dON3PTCN5w4XcSrd/VnwpjeNE44404IpoZYsghSj1yajIgwea7/u/G16XDeaWZtPMANfdpa0cAQ16x+LHdc1JGP1+xl8/7jNbbcxdsOcflzC3nl6538bEAHvho3hEu72Y6Hv1myCFItE+P42YAOvL8yi12HTgQ6nBrz0eq9TtHA/jawHQ7uH9KZhJgonpu95ayXlXOqkCffX8tPX/6WqIgIpt83kD+NOp8GVvivVliyCGIPDu1CTGSEX7rxgaCqpC3PoFfbRM5paUUDw0HjhBjuHtSJL9bvZ31WTrWXM3PDfkZMWMCMFZk8cEkXvhg7mAGd7fqc2mTJIoglNYjl5xd14KPVWWw9UHPd+EBZm5nD5gNWNDDc/GJQJxrGRTFxlu+9i+zjp3n47ZXcN20FTevH8tFDF/PklecQF22HMGubJYsg98CQLm43vu73Lqanu0UDe1vRwHCSWC+a+y/pwpxNB1m156hX86gqH67KZMTEBczacIBfjkzh40cupkfbRD9HaypiySLINU6I4e6LO/LZun1s2Fv9bnygnSoo5pPVe7mqRyu7uUwYuvOijjRJiGGCF72LrGOnuOu15YybvobOzRL4fOwgHhmWTHSkba4CydZ+HfCLwZ3dbnzd7V18sd4tGmiHoMJSQmwUD17ShUVbD7Fs5xGPbUpKlGlLdzNywgKW7TzC7689lxkPXETX5lb4LxhYsqgDEutFc9+Qzsz+7gBrMo4FOpxqmb48gw5N4xnYuUmgQzEB8rOBHUhqEMs/Zm7GKQTxgx3Zedw6dSm//Wg9fTs05qvHh3DnxVb4L5hYsqgj7ry4E43jo3m2GoOEgbbr0Am+3ekUDbQra8NXvZhIHh7ahWU7j7B422HAKfz3wvztXDFpEZv25/L3m3vyxt0X0K6JFf4LNn5LFiKSKCJfiMhMEfnQvSe3p3aviMgSEXnaX7GEgvqxUTw4tAsLt2SzfJfnbnywmrEigwiBm/rZtRXh7icD2tM6MY5nZ21mw94cRj2/mL99uYlh3Zoze/wl3JLaznYogpQ/exY/BSao6kicGyhdUb6BiNwIRKrqhUBnEUn2Yzx13u0DO5LUIJZnZ24OdChe+75oYIoVDTQQGxXJI8OSWbXnGNdM/pr9Oad54ad9efH2fjS3wn9BzW/JQlWfV9VZ7ssk4KCHZkOBNPf5TGBQ+QYicp+IpItIenZ2tl9irStKu/FLdxzhm211o9jvwi3ZHMg9zRgb2DauW1LbcmHnptzcty2zxw/hyh6tAh2S8YLfxyxE5EKgsaou9fB2ApDlPj8CtCjfQFWnqmqqqqYmJSX5MdK64dYL2tMqMY5nZ205Y5AwGKWlZ9AkIYbLup/xqzVhKjoygnfuG8jfb+lFo3gr/FdX+DVZiEgTYDJwdwVN8oDSYxP1/R1PKIiLjuSRYV1Zsfso87cEd0/rcN5pZn93gBv6tCEmyn61xtRl/hzgjgFmAL9W1d0VNFvBD4eeegG7/BVPKLmlXzvaNanHhJnB3bv4cFWWUzTQDkEZU+f5c3fvF0Bf4CkRmS8ivxORZ8q1+Qi4XUQmAKOBz/wYT8iIiYrgsWHJrMvKYebGA4EOxyNVJS09g17tGtGtpV1UZUxd588B7hdUtbGqDnUff1DVp8u1ycUZ5F4KXKqqdbeeRS27oU8bOjdLYOKsLZSUBF/vYk1mDlsO5DHa7rFtTEgI+IFkVT2qqmmquj/QsdQlUZERjB2ezKb9x/l8/b5Ah3OG6csziIuO4NpeVjTQmFAQ8GRhqu/anq1JaVGfibO2UBxEvYuTBUV8ssaKBhoTSixZ1GEREcL4ESlszz7Bf1ZnVT1DLfli3X7yrGigMSHFkkUdd/l5LTmvdUOem72VwuKSQIcDOPet6Ng0ngGdrGigMaHCkkUdJ+L0LvYcOcn7KzIDHQ47D51g2c4jVuPHmBBjySIEDDunOb3bNWLy3G2cLioOaCwz0t2igX3tLChjQoklixAgIjwxMoWsY6eYvjwjYHEUFZfw/spMLklJomWiFYUzJpRYsggRg7o244JOTZgydxv5hYHpXSzc6hYN7G8D28aEGksWIUJEeGJECgePn+bNpRVVV/GvtOWZNE2IYdg5VjTQmFBjySKEDOjclEFdm/HC/O2cOF1Uq599yIoGGhPS7L86xIwfmcLhEwW8vmRXrX7uR6uyKCpRRtshKGNCkiWLENO3fWOGndOclxbsIDe/sFY+U1WZvjyD3u0akdLCigYaE4osWYSg8SNSyDlVyL+/3lkrn7c64xhbD+bZFdvGhDBLFiHo/DaJXHFeS15ZtJNjJwv8/nlp6RnUi47k2l52e0xjQpUlixA1bkQKeQVFTF24w6+f4xQN3MdVPVrRwIoGGhOyLFmEqG4tG3Btz9a89s0uDuWd9tvnfP590UC7YtuYUObve3C3EJFFlbzfRkQy3TvpzReRJH/GE27GDk8mv7CYF+dv99tnpC13igZeYEUDjQlp/rwHd2PgdSChkmYDgD+XuZtetr/iCUddkupzY9+2TFu6mwO5+TW+/B3ZeSzbZUUDjQkH/uxZFANjgNxK2gwE7hGRlSLyFz/GErbGXpZMcYny/LxtNb7sGSsyiRC4uZ8dgjIm1PnzHty5XtxT+wuce3D3By4UkZ7lG4jIfSKSLiLp2dnW8fBVuybx3JLajneWZZB17FSNLbeouIT3V2QytFtzWjS0ooHGhLpAD3B/o6rHVbUYWAUkl2+gqlNVNVVVU5OSbEijOh4d1hWAKXO31tgyF2zJ5uDx03ZthTFhItDJ4isRaSUi8cBIYH2A4wlJrRvV47YB7UlLz2T34RM1ssy09Ay3aGDzGlmeMSa41VqyEJFhIvJIucl/AOYBS4EXVXVzbcUTbh4a2oWoCGHSnLPvXWQfP82c7w5yY18rGmhMuPD7f7qqDnV/zlXVKeXem6eq56hqz/LvmZrVvGEcd1zUkY9WZbHtYN5ZLev7ooF2CMqYsGG7hWHk/iGdiYuO5LnZW6q9DFVlenoGfdo3ItmKBhoTNixZhJGm9WO5++JOfLp2H9/tq+yM5oqt3HOMbQfzGGO9CmPCiiWLMHPv4M40iIti4qzq9S5muEUDr+5pRQONCSeWLMJMYnw09wzqzMyNB1iXWdVlMD924nQRn6zZy9U9rWigMeHGkkUYuntQRxrFRzNhlm8nn32+bh8nCoptYNuYMGTJIgw1iIvm/iFdmLc5mxW7j3o9X1p6Bp2aJdC/Y2M/RmeMCUaWLMLUHRd1oFn9GK97Fzuy81i+6yi3pLa1ooHGhCFLFmEqPiaKB4d2ZfG2wyzZfrjK9mnpmURGCDf3taKBxoQjSxZh7KcD2tOiYSwTZm1GVStsV1RcwvsrMxmakkRzKxpoTFiyZBHG4qIjeWRYMst3HWXR1kMVtpu/OZvs46cZ3d8Gto0JV5YswtyY1Ha0aVSPZ2dW3LtIS8+gWX0rGmhMOLNkEeZioiIYe1kyazJzmPPdwTPezz5+mrmbDnJj37ZER9qfizHhyv77DTf2bUPHpvFMmLWFkpIf9y4+WJnpFg20gW1jwpklC0NUZARjhyezcV8uX27Y//10VSUtPYO+7RvRtbkVDTQmnFmyMABc16sNXZvXZ+KsLRS7vYuVe46yPfsEY2xg25iwZ8nCABAZIYwbnsLWg3l8smYvAGnLM4mPieTqnq0DHJ0xJtAsWZjvXXl+S7q3asikOVvJOVXIp2v3cnWPVtSPjQp0aMaYAPNrshCRFiKyqJL3o0XkExFZLCJ3+zMWU7WICGH8iBR2HjrBQ2+tcIoG2iEoYwx+TBYi0hh4HUiopNmjwApVvRi4WURsFDXAhndvTq+2iSzedpjOzRJI7WBFA40x/u1ZFANjgMpuyTYUSHOfLwRS/RiP8YKIMH5kNwBG929nRQONMQD47WC0quYCVW1sEoAs9/kRoEX5BiJyH3AfQPv27Ws2SOPRJSlJTL9vIH3aW6/CGOMI9AB3HlDPfV4fD/Go6lRVTVXV1KSkpFoNLpwN6NyUmKhA/3kYY4JFoLcGK4BB7vNewK7AhWKMMaYitXZOpIgMA85V1SllJr8OfC4ig4FzgW9rKx5jjDHe83vPQlWHuj/nlksUqOpuYASwGBiuqsX+jscYY4zvAn61laru5YczoowxxgShQI9ZGGOMqQMsWRhjjKmSJQtjjDFVkopupRmMRCQb2H0Wi2gGVHyzaVOerS/f2Pryja0v35zN+uqgqmd1oVqdShZnS0TSVdVKinjJ1pdvbH35xtaXbwK9vuwwlDHGmCpZsjDGGFOlcEsWUwMdQB1j68s3tr58Y+vLNwFdX2E1ZmGMMaZ6wq1nYYwxphosWRhjTBASkUYi0tqH9vX9GU9IJwtfV7YXy2sgIueUeZ0sIq1qavnGGFPGOOC33jQUkb7ApyISU8H7IiKNRaSriAwUkWtFZJyIvCki3bz5jJBOFvi2sv9WNrGIyO9FpF25Zg2BaSJSWoDx70DLGon0LIlICxFZ5D7vKyKzRWSxiDzhTvuDiMx3H5tE5Nee2lWy/O4i8p8yr0e4y1oiIj+pZFojEVnofsaV/lsDvvFifXmaFi0in7jT7q5k2e3d9TBXRKa6/6hnzOvttEATkUQR+UJEZorIhyISIyKvuL/np8u082qaN8uvZHnf/97c10GxvnxYR17F727AHwNSRORTEVnh/vxSRN4t//mquhJ4B6jodqL/BcwF/grcAfQA9gOT3Z9VU9WgeeDcVnWR+7wvMBunfPkT7rQ/APPdxybg157auW27AUeBOcCnODda+hT4Eni3TLv3gEhgGPANEONO3wg0d59PcpczG1jk/pwDHCvz/J4ArrfG7vda6b5eDLQDxP1Oncq1fw9oU1W7Mu27uOtuvvs6ElgLNADigM04t8gtPy0O+CdwpzvPPNyTKgL8d1bl+qpg2njg9+48nwMNKlj+n4Hu7vMvgJ6e5vV2WhCsr4eAEe7zF4CfA6+5r/8NJAM3ejPNy+VfV8HyfvR7c98LivXl5TryKn6cndI1wK1l2i318JkxwHJ+2CaWPjYCc8q1fRJn+zncw+NyILGq7xjwEuWlRKQxzs2QEtxJk4FbgUxgsYh8oKq/K9P+PeANnPLmP2oHHHanP6iq77rtl6rqNR4+OsKwJJAAAAcMSURBVFmd+2jMFZHPcH7JLwP1VfWg2+ZJ4Hwg1n19QlVXucttjZPNV9fAaqiuYmAMULrn30RVMwBE5DDOHx/u6/5ApqpmiUiF7co5DtwEfOW+bgDkqepxd97TOH+45afVA4YAv1HVYhHZDHQEdtbIt64+b9aXp2lDcf4WABYCqTgJ8EdU9akyL5vilGjwNK+30874jNqkqs+XeZkE/Ax4zn09E+dul3344VYDlU3b6sXyDwK3eZj3fX78e4MgWV9eriNv4z8CTAOuFZGfue91E5HPcW4rMVVV31PVAqB/2ThEZBTOTvRd5UKMBQoqCD8SZ6eoUkGTLKjZDV5DvFjZ7vSy5w7/j6qWiDNQdLR0oqqeEpEpOIedAMaJyGPA9Th/mENVdWnNrAbfqWougMj3v+/FIvIIzh9dR5w9/lJjgd9V1M491JRYpv3bqjq17PJV9ZiI5IjIrTj1ag6q6tEKphWpap67rCM4vceAJgsv15enaQlAljvPEaBFFetrDLBBVfeKyBnzelpeBdOCgohciLN3vIsfx9iXM+P2OE1EXsLp9Zeaq6p/LLt8VV0qIveWn9fD7w0PnxHQ9VXZOvI2flWdB6wRkYWqOsSdZ6mqXlXFZ98K/ALnRnLHy73dCCf5PoOTHMqaoKrHqvpuQZMsanKDp06/y6eVLSKJOAkrD7gUpxtY3nD3ZwlOJo7HSTbBdrHK/Tjf4Y/A39z1gYg0wjm0tr2Sdtd7+RmjgMtw/vjuqmRa2bsf1ic4x8nOWA8i4mlaHk5vKQfnu+Spqsf1JSKdgV/yw9/MGfP6MC3gRKQJTm//JpxDJ/Xct0p/p3neTFPV+71YPhUsz5OgWV9erCNPzohfnDHREirYrohIBICqlpSZdi9wA/z/9s4lRI4qCsPfmZkwikbBBBTDYNAsAgZGdBMIkgmKKKghoBEhEN+PENCVGBCjO90oioIaELKIElwI2iK+QDIbxQcoDsEHQRAcQfCBCx2c5Lg4t+yamuqu6umedDX5Pyi66vS9t/qequ7T95xb53Kzu/9dUm0K+IFwA8/k6u0h/txV0sQvbsb9RFxiP9U/eEvKmdlEUmhHZWcKT8drCZ/8jiS6g/Dr5/mHGC4+Ciz0373VI7nVvk2HR3Jv7ST8olXl6pxjgXCvfO3us51kwJyZZcnPpukva/CqUKaHDrr5gnAnQPTlx7L2kkv1deAud/+zS926sqGSAs5vAAc8lkIeaF9K2qdu3R7KrSo1dVRGWbnHiO/pXymo3SI8I9n+O8CN6bxjZvY4EefZVWYozGwcmHL3XwkjVKRMtpzVDPqsZCMFUdP+FBFctJxsL/Bwoc6ScsATRCCpldt+z+2/S1hgiFjDMWBnOr4XmCP8jGflzvEJMZPgqVR+K+GTnCEFqIa9FXR3GLi68P5rxHCYbuXqtJ+O3wcu6yYjfKpfEakKWsPWUY/6WiIDLkn3xnNEYHG8Q7tPA/O0A47by+rWlTVATw+m70/Wn73pmj4DHCfccOfVkdVs/7ZudQvXrRH6qqOjfj4/8GmJ7FzgA+Ao4Q6/ATinUMaIUf7L6XiemJSTbd8Ad9bq47BvxBIF5BU5sB+8DspeA/wMXEcEIp8n/h1MEtPWWtnFA47l6n2U299BQ4xFUzdgE3ALMDnszzKAvlwM7O70w9dr3bqypm2EX343cFGvsn7OMehrcrp1tNLPT8S+irKNwN1p/1LghWSgThBxwd+AbcREg02p3GyhjT3Avjr9OWNyQ5nZnLtfXiJfT1jfWeBZ4JAnX6CZHQAOewQov3T3K83sFeAPd3/EzDYAbwEH3b11+nojhBC9Y2bne9s12lvdM8VYVGFmY54LGAkhhGgjYyGEEKKSJs+GEkII0RBkLIQYEGa2zto5sdZY4ekrIUYZuaGE6AMzewhYcPeXzGwS+A64iUi5cCHtOexXARtXGlwUYtg05gluIZqKmW0npmx/D2x293ym4UXg3/Tg0wXEk7u/uPvthTY+pnNuHiEaj4yFENUsAm+6+34z+yylkt6S5NPE6GERuM/dt5nZe8l4ZFyfXjWMFyOLjIUQ1ZwEdpnZFiLVzKtmNuXuP5nZA0QqmKO082FNuPs1ECMKd19U+EKMOgpwC1HNSWJkMQPMm9nZwNsp+WQZmy0WTvqQGHkIMfJoZCFENfk/VeaRsv5FYu2BMo67+7Xwf6xCiJFHxkKIaiZou6E2ALj7IYh12EvKX5FGFQDT1l6GV4iRRTexENWM0w5wHyy8lwUjxrJ9d1+2PkBKYa10MmJkkbEQoprPSWtbuPuTmdDMbiWyE99DpIueLKtsZkeIoLemzoqRRQ/lCbFCUqD7lMeiT93KrfXly1wKMVLIWAghhKhEU2eFEEJUImMhhBCiEhkLIYQQlchYCCGEqETGQgghRCX/Ac27tygthgb0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 1, 3, 2, 7]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析出版年份，对应表格中的第三列(content[2])\n",
    "ana_year(\"红楼梦\",content1[2])\n",
    "ana_year(\"西游记\",content2[2])\n",
    "ana_year(\"水浒传\",content3[2])\n",
    "ana_year(\"三国演义\",content4[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，分析四大名著的评价人数和得分情况\n",
    "def ana_mark(people, mark):\n",
    "    sum_ = [0,0,0,0]\n",
    "    mark_ = [0,0,0,0]\n",
    "    for i in range(4):\n",
    "        for data in people[i]:\n",
    "            if data == 0:\n",
    "                contimue\n",
    "            else:\n",
    "                sum_[i] += int(data)\n",
    "        for data in mark[i]:\n",
    "            if data == 0:\n",
    "                contimue\n",
    "            else:\n",
    "                mark_[i] += float(data)\n",
    "        sum_[i] = sum_[i] / len(people[i])\n",
    "        mark_[i] = mark_[i] / len(mark[i])\n",
    "    print(sum_)\n",
    "    drawmark(mark_)\n",
    "    draw_mark(sum_,mark_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，画四大名著得分比较的柱状图\n",
    "def autolabel(rects):#在柱状图上显示数量坐标\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        plt.text(rect.get_x()+0.14, 1.03*height, '%s' % height)\n",
    "        \n",
    "def drawmark(mark):\n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    \n",
    "    plt.title(\"四大名著相关出版物平均得分统计\")\n",
    "    name_list = ['红楼梦', '西游记', '水浒传', '三国演义']\n",
    "    plt.xlabel(\"书名\")\n",
    "    plt.ylabel(\"得分\")\n",
    "\n",
    "    plt.xticks((0,1,2,3),('红楼梦', '西游记', '水浒传', '三国演义'))\n",
    "    #print(type(mark[0]))\n",
    "\n",
    "    #plt.bar(x = (0,1,2,3),height = distance,width = 0.35,align=\"center\")\n",
    "    #my_y_ticks = np.arange(0, 7, 0.05)\n",
    "    #plt.yticks(my_y_ticks)\n",
    "    for i in range(4):\n",
    "        mark[i] = round(mark[i],2)  #将评分保留至小数点后两位\n",
    "        #print(data)\n",
    "    print(mark)\n",
    "    rect = plt.bar(x = (0,1,2,3),height = mark,width = 0.35,align=\"center\",color = 'orange')\n",
    "    #rect = plt.bar(x = (0,1,2,3,4),height = count,width = 0.35,align=\"center\")\n",
    "    height = mark\n",
    "    #print(type(height[0]))\n",
    "    autolabel(rect)  #显示柱状图的数值\n",
    "\n",
    "    plt.show()\n",
    "    #print(count)\n",
    "    #return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，绘制并列柱状图，分析评价人数和得分\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def autolabel2(rects2):#在柱状图上显示数量坐标\n",
    "    for rect in rects2:\n",
    "        height = round(rect.get_height(),2)\n",
    "        print(height)\n",
    "        plt.text(rect.get_x()+0.05, 1.03*height, '%s' % round(height/5000,2))   #设置显示坐标位置和数值\n",
    "                 \n",
    "def draw_mark(sum_, mark_):\n",
    "    name_list = ['红楼梦', '西游记', '水浒传', '三国演义']\n",
    "    y_list = sum_\n",
    "    for i in range(4):\n",
    "        mark_[i] = round(mark_[i],2)  #将评分保留至小数点后两位\n",
    "        #print(mark_[i])\n",
    "        mark_[i] = 5000 * mark_[i]\n",
    "        #print(mark_[i])\n",
    "    #print(sum_)\n",
    "        \n",
    "    #print(mark_)\n",
    "    y_list2 = mark_\n",
    "    #bar_width = 0.3\n",
    "    #size = 4\n",
    "    #x = np.random.random(len(x_data))\n",
    "    x =list(range(len(y_list)))\n",
    "    total_width, n = 0.8, 2  \n",
    "    width = total_width / n  \n",
    "    #绘制柱状图\n",
    "     \n",
    "    # 在柱状图上显示具体数值, ha参数控制水平对齐方式, va控制垂直对齐方式\n",
    "    #for x, y in enumerate(y_data):\n",
    "        #plt.text(x, y + 100, '%s' % y, ha='center', va='bottom')\n",
    "    #for x, y in enumerate(y_data2):\n",
    "        #plt.text(x+bar_width, y + 100, '%s' % y, ha='center', va='top')\n",
    "    rect = plt.bar(x, y_list, width=width, label='平均评论人数',fc = 'y')  \n",
    "    for i in range(len(x)):  \n",
    "        x[i] = x[i] + width  \n",
    "    rect2 = plt.bar(x, y_list2, width=width, label='平均得分',tick_label = name_list,fc = 'r')  \n",
    "    plt.legend()  \n",
    "    #plt.show()  \n",
    "    #设置标题\n",
    "    plt.title(\"豆瓣网四大名著相关评论数和得分情况\")\n",
    "    plt.xlabel(\"书籍\")\n",
    "    plt.ylabel(\"数量\")\n",
    "    autolabel2(rect2)\n",
    "    #plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17413.0, 113509.4, 7934.133333333333, 12594.625]\n",
      "[6.42, 6.48, 5.23, 6.99]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32100.0\n",
      "32400.0\n",
      "26150.0\n",
      "34950.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分析四大名著相关出版物平均得分和评价人数\n",
    "people = [content1[3],content2[3],content3[3],content4[3]]\n",
    "mark = [content1[4],content2[4],content3[4],content4[4]]\n",
    "ana_mark(people,mark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ana_typemark(book, type_, num, mark_):   #type_为各类书籍的类型，mark_为得分列表,num为不同类型的产品数量\n",
    "    type_dict = {}\n",
    "    for i in range(1,len(type_)):#遍历列表中的各个元素\n",
    "        if type_[i] not in type_dict.keys():   #如果该key不存在，则将value值初始化为该组数据得分\n",
    "            type_dict[type_[i]] = float(mark_[i]) \n",
    "        else:     #如果该key存在，则将value值加1\n",
    "            type_dict[type_[i]] += float(mark_[i])    \n",
    "\n",
    "    sum_ = []\n",
    "    typename = []  #类型名称列表\n",
    "    average = []   #各个类型对应的平均分\n",
    "    explode = []\n",
    "    for key,value in type_dict.items():\n",
    "        typename.append(key)\n",
    "        sum_.append(value)\n",
    "        explode.append(0)\n",
    "    i = 0\n",
    "    for key,value in type_dict.items():\n",
    "        average.append(value/num[i])\n",
    "        i += 1\n",
    "    explode = tuple(explode)\n",
    "    drawtype(book,typename, average,explode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义函数，画四大名著不同类型得分比较的柱状图\n",
    "def drawtype(book,typename, average,explode):\n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    \n",
    "    plt.title(\"《\" + book + \"》不同类型出版物平均得分统计\")\n",
    "    name_list = typename\n",
    "    plt.xlabel(\"类型\")\n",
    "    plt.ylabel(\"平均得分\")\n",
    "    list1 = []\n",
    "    for i in range(len(typename)):\n",
    "        list1.append(i)\n",
    "    list1 = tuple(list1)\n",
    "    typename = tuple(typename)\n",
    "    #plt.xticks((0,1,2,3),('红楼梦', '西游记', '水浒传', '三国演义'))\n",
    "    plt.xticks(list1,typename)  #参数要求为元组，强制类型转换\n",
    "    #print(type(mark[0]))\n",
    "\n",
    "    #plt.bar(x = (0,1,2,3),height = distance,width = 0.35,align=\"center\")\n",
    "    #my_y_ticks = np.arange(0, 7, 0.05)\n",
    "    #plt.yticks(my_y_ticks)\n",
    "    for i in range(len(typename)):\n",
    "        average[i] = round(average[i],2)  #将评分保留至小数点后两位\n",
    "        #print(data)\n",
    "    print(average)\n",
    "    rect = plt.bar(x = list1,height = average,width = 0.35,align=\"center\",color = 'lightblue')\n",
    "    #rect = plt.bar(x = (0,1,2,3,4),height = count,width = 0.35,align=\"center\")\n",
    "    height = average\n",
    "    #print(type(height[0]))\n",
    "    autolabel(rect)  #显示柱状图的数值\n",
    "\n",
    "    plt.show()\n",
    "    #print(count)\n",
    "    #return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.71, 9.55, 5.3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.48, 6.53]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.61, 8.85, 3.5, 9.3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.17, 9.3, 4.4, 6.07]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ana_typemark(\"红楼梦\", content1[0], num1, content1[4])\n",
    "ana_typemark(\"西游记\", content2[0], num2, content2[4])\n",
    "ana_typemark(\"水浒传\", content3[0], num3, content3[4])\n",
    "ana_typemark(\"三国演义\", content4[0], num4, content4[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "西游记相关景点数量：\n",
      "126\n",
      "红楼梦相关景点数量：\n",
      "2\n",
      "水浒传相关景点数量：\n",
      "0\n",
      "三国演义相关景点数量：\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "def travelnum(place):\n",
    "    Num = 0\n",
    "    for i in range(10):  #四类景区的搜索结果页数均不超过10\n",
    "        url = \"https://vacations.ctrip.com/whole-1B126P\" + str(i+1) + \"/?searchvalue=\" + place    #翻页，循环爬取        \n",
    "        # 将cookies字符串组装为字典\n",
    "        cookies_str = '复制粘贴对应网站cookies'\n",
    "        cookies_dict = {}       \n",
    "        # 其他请求头参数\n",
    "        headers = {            \n",
    "             'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36'\n",
    "        }       \n",
    "        # 访问页面\n",
    "        page = requests.get(url=url, cookies=cookies_dict, headers=headers)\n",
    "        soup = BeautifulSoup(page.text, 'lxml')\n",
    "        all_divs = soup.find_all('div', class_='product_main')\n",
    "        travel_divs = [div.get_text() for div in all_divs]\n",
    "        num = len(travel_divs)\n",
    "        #print(num)\n",
    "        Num += num\n",
    "        if num < 30:\n",
    "            break\n",
    "    return Num\n",
    "       \n",
    "num1 = travelnum('西游记')\n",
    "print(\"西游记相关景点数量：\")\n",
    "print(num1)\n",
    "num2 = travelnum('红楼梦')\n",
    "print(\"红楼梦相关景点数量：\")\n",
    "print(num2)\n",
    "num3= travelnum('水浒传')\n",
    "print(\"水浒传相关景点数量：\")\n",
    "print(num3)\n",
    "num4 = travelnum('三国演义')\n",
    "print(\"三国演义相关景点数量：\")\n",
    "print(num4)\n",
    "content = [num1,num2,num3,num4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[126, 2, 0, 5]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "from pylab import mpl#字体\n",
    "#设置字体\n",
    "from matplotlib import mlab\n",
    "from matplotlib import rcParams\n",
    "\n",
    "def draw(count):#画柱状图   \n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei']         \n",
    "    plt.title(\"四大名著相关景点数量\")\n",
    "    name_list = ['西游记', '红楼梦', '水浒传', '三国演义']\n",
    "    #number=[dis1,dis2,dis3,dis4]\n",
    "    plt.xlabel(\"名称\")\n",
    "    plt.ylabel(\"景点数量\")\n",
    "\n",
    "    plt.xticks((0,1,2,3),('西游记', '红楼梦', '水浒传', '三国演义'))\n",
    "\n",
    "    #plt.bar(x = (0,1,2,3),height = distance,width = 0.35,align=\"center\")\n",
    "\n",
    "    rect = plt.bar(x = (0,1,2,3),height = count,width = 0.35,align=\"center\")\n",
    "    rect = plt.bar(x = (0,1,2,3),height = count,width = 0.35,align=\"center\")\n",
    "    autolabel(rect)\n",
    "\n",
    "    plt.show()\n",
    "    #print(count)\n",
    "    return count\n",
    "draw(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "《红楼梦》中出现频率最高的二十个词组为：\n",
      "字符\t字频\n",
      "宝玉\t3852\n",
      "笑道\t2316\n",
      "太太\t1861\n",
      "什么\t1811\n",
      "凤姐\t1719\n",
      "贾母\t1624\n",
      "黛玉\t1312\n",
      "那里\t1156\n",
      "姑娘\t1102\n",
      "怎么\t1074\n",
      "宝钗\t1038\n",
      "王夫\t1010\n",
      "丫头\t995\n",
      "说道\t937\n",
      "知道\t936\n",
      "老太\t934\n",
      "贾政\t919\n",
      "如今\t918\n",
      "奶奶\t873\n",
      "自己\t794\n",
      "《西游记》中出现频率最高的二十个词组为：\n",
      "字符\t字频\n",
      "行者\t4188\n",
      "八戒\t1746\n",
      "者道\t1637\n",
      "师父\t1553\n",
      "三藏\t1287\n",
      "大圣\t1047\n",
      "唐僧\t972\n",
      "沙僧\t780\n",
      "和尚\t744\n",
      "笑道\t717\n",
      "那里\t710\n",
      "怎么\t709\n",
      "菩萨\t625\n",
      "长老\t625\n",
      "妖精\t604\n",
      "老孙\t561\n",
      "戒道\t559\n",
      "什么\t478\n",
      "国王\t455\n",
      "见那\t438\n",
      "《水浒传》中出现频率最高的二十个词组为：\n",
      "字符\t字频\n",
      "宋江\t3658\n",
      "李逵\t1093\n",
      "武松\t1030\n",
      "只见\t903\n",
      "如何\t890\n",
      "那里\t840\n",
      "哥哥\t754\n",
      "林冲\t754\n",
      "军马\t735\n",
      "头领\t689\n",
      "说道\t685\n",
      "吴用\t681\n",
      "太尉\t643\n",
      "听得\t635\n",
      "智深\t623\n",
      "兄弟\t615\n",
      "戴宗\t596\n",
      "梁山\t558\n",
      "俊义\t550\n",
      "卢俊\t548\n",
      "《三国演义》中出现频率最高的二十个词组为：\n",
      "字符\t字频\n",
      "玄德\t1813\n",
      "孔明\t1689\n",
      "将军\t942\n",
      "曹操\t941\n",
      "却说\t649\n",
      "司马\t575\n",
      "丞相\t547\n",
      "关公\t519\n",
      "引兵\t486\n",
      "云长\t443\n",
      "荆州\t420\n",
      "蜀兵\t391\n",
      "夏侯\t387\n",
      "大喜\t379\n",
      "张飞\t365\n",
      "吕布\t365\n",
      "诸葛\t347\n",
      "商议\t344\n",
      "如何\t337\n",
      "大怒\t337\n"
     ]
    }
   ],
   "source": [
    "#分别统计四本书中出现次数最多的二十个词组\n",
    "import os\n",
    "def maxword(book, path):\n",
    "    exclude_str = \"，。！？、（）【】<>《》=：+-*—“”‘’… ；\\n了你我她他的\\u3000不一来人这儿是下此着个子下上曰之有\"   #除去标点符号和无用字符\n",
    "    f = open(path,encoding = 'gb18030')\n",
    "    content = []\n",
    "   \n",
    "    for line in f:   #将每个词组存储到列表中\n",
    "        #line = list(line)\n",
    "        for i in range(len(line)-1):    #除去标点符号和无用字符\n",
    "            if line[i] not in exclude_str:\n",
    "                if line[i+1] not in exclude_str:\n",
    "                    content.append(line[i]+line[i+1])\n",
    "            \n",
    "    count={}  #统计每个字出现的数量\n",
    "    for character in content:\n",
    "        count.setdefault(character,0)    #如果键不存在于字典中，将会添加键并将值设为默认值\n",
    "        count[character] = count[character] + 1\n",
    "     # 排序\n",
    "    # x[1]是按字频排序，x[0]则是按字排序\n",
    "    lstWords = sorted(count.items(), key=lambda x:x[1],  reverse=True) \n",
    "    print(book + \"中出现频率最高的二十个词组为：\")\n",
    "    print ('字符\\t字频')\n",
    "    for e in lstWords[:20]:\n",
    "        #print(e)\n",
    "        print ('%s\\t%d' % e)\n",
    "    #print(count)\n",
    "    f.close()\n",
    "    \n",
    "maxword(\"《红楼梦》\", \"D:\\python\\大作业\\红楼梦.txt\")\n",
    "maxword(\"《西游记》\", r\"D:\\python\\大作业\\西游记.txt\")\n",
    "maxword(\"《水浒传》\", r\"D:\\python\\大作业\\水浒传.txt\")\n",
    "maxword(\"《三国演义》\", r\"D:\\python\\大作业\\三国演义.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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